Alzheimer's disease is a debilitating disorder marked by a decline in cognitive function. Timely identification of the disease is essential for the development of personalized treatment strategies that aim to mitigate its progression. The application of generated images for the prediction of Alzheimer's disease poses challenges, particularly in accurately representing the disease's characteristics when input sequences are captured at irregular time intervals. This study presents an innovative methodology for sequential image generation, guided by quantitative metrics, to maintain the essential features indicative of disease progression. Furthermore, an age-scaling factor is integrated into the process to produce age-specific MRI images, facilitating the prediction of advanced stages of the disease. The results obtained from the ablation study suggest that the inclusion of quantitative metrics significantly improves the accuracy of MRI image synthesis. Furthermore, the application of age-scaled pixel loss contributed to the enhanced iterative generation of MRI images. In terms of long-term disease prognosis, the Structural Similarity Index reached a peak value of 0.882, indicating a substantial degree of similarity in the synthesized images.

翻译：阿尔茨海默病是一种以认知功能衰退为特征的进行性神经系统疾病。及时识别该疾病对于制定旨在延缓其进展的个性化治疗策略至关重要。利用生成图像进行阿尔茨海默病预测面临挑战，尤其是在输入序列以不规则时间间隔采集时，如何准确表征疾病特征。本研究提出一种创新的序列图像生成方法，通过量化指标引导，以保持指示疾病进展的关键特征。此外，该过程整合了年龄缩放因子，以生成年龄特异性的磁共振成像图像，从而促进疾病晚期阶段的预测。消融实验结果表明，量化指标的引入显著提升了磁共振成像图像合成的准确性。同时，应用年龄缩放像素损失有助于增强磁共振成像图像的迭代生成质量。在长期疾病预后方面，结构相似性指数达到峰值0.882，表明合成图像具有高度的相似性。